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  • 1. Towards Ontology based Agricultural Knowledge Services
    • Asanee Kawtrakul
    • At FAO, Italy
    • 21 September 2007
  • 2. National Electronic and Computer Technology Center National Science and Technology Development Agency
  • 3. Outline
    • Why need Agricultural Knowledge Service?
    • Who are the target of the Services?
    • Three steps for providing the knowledge service
    • Q&A system:Case Studies on Rice.
    • Conclusion: Current and Next Steps
  • 4. Why need Agricutural Knowledge Services?
    • Information Scattered
    • Information Overload
    • Solutions: Assumption: Knowledge derived from Appropriate Information
    • Right Information (Necessary and Sufficient) for Right Purpose
    • Right Information Representation for the good Imaginary
  • 5. Who are the Target of Knowledge services? Farmer, Naïve People, SME- Business Domain Specific Researchers,Knowledge Broker Related area Researchers Policy Decision Maker
  • 6. Three steps for providing the knowledge service
    • Ontology Base Construction and Maintenance with Good Acquisition Tool or Workbench
    • Content Management with Good Browser
    • Knowledge Service with Good Devices
  • 7. System management module
    • User management
    • Group management
    • System preference
    • System Statistic Report
    Ontological Knowledge Authoring Tools Structured Corpus Dictionary Morphological Analysis Merging & Organizing Verification System Ontology Thesaurus Taxonomic, Part-of, Synonym Relationship Acquisition by using Pattern Relationship Refinement Relationship Acquisition (Phrase Level) Structure Analysis Non-Taxonomic Relationship Acquisition (Sentence Level) System Data Repository Sesame API JDBC API SeRQL SQL Validation System Statistic Report User management Group management System preference GUI for Ontology Acquisition & Maintenance Task-Oriented Parsing Ontology Extraction Lexicon Information Integration Filtering & Correcting Morphological Analysis and Phrase chunking MRD Ontology Integration Concept management Relationship management Search Scheme management Import Export Consistency check User Management Ontological Knowledge Management Authoring Tools Communities Text Ontology Repository in OWL format (MySQL) Printed Dictionaries Raw Text
  • 8. An Example of Rice Ontology Merging Plant products Khao Dore Jasmine rice Luang Patiw Cereals Rice Maize ko-kho15 Jasmine rice 105 Jasmine rice Tung kula ronghai Luang Patiw Paddy Processed plant products
  • 9. Towards Knowledge management with good Content management and Knowledge Service Tool
  • 10. Business Researchers Government Farmers Harvest Technology Organization Based Pest WWW Rice Variety Disease Knowledge Portal Construction Knowledge Service Provision Knowledge Extraction & generalization Information Extraction & Integration Inference Engine Knowledge Tracking Query Processing Structured Knowledge & rules Document Warehouse Ontology Acquisition Distributed Information Collection Preriodic web crawler Task Oriented Ontology & Linguistic Knowledge Meta Data Rice Variety
  • 11. Q & A System Different Answer for Different Need
  • 12.
    • For Knowledge Broker
    • Economic Plants -> Rice and Sugar
      • Types of rice -> Jasmine and Basmati
      • Jasmine -> 2 types Jasmine 105 and Jasmine 192
      • Jasmine 105 -> can have Pest 1 and Pest 2
            • Pest 1 -> can be controlled using Method 1 and Method 2
            • Pest 2 -> can be controlled using Method 3
  • 13.
    • For Researchers
    Research Organization Kasetsart University FAO NII Author 1 Author 2 Author 3 Author 4 Author 5 Author 6 Author 7 Author 10
    • D1.pdf
    • D2.htm
    • D3.doc
    • D4.doc
    • D5.pdf
    • D6.pdf
    • D7.rdf
    • D8.xml
    • D9.xls
    • D10.htm
    • D11.htm
    • D12.htm
    • D13.ps
    • D14.doc
    • D15.txt
    • D16.pdf
    • D17.pdf
    • D18.pdf
    • D19.jpg
    • D20.jpg
    • D21.rdf
    • D22.xtm
    • D23.xml
    • D24.pdf
    • D25.pdf
    • D26.doc
    • D27.doc
    • D28.doc
    • D29
    • D30
    • D31
    • D32
    • D33
    • D34
    • D35.sql
    • D36.xml
    • D45.ppt
    • D35
    • D36
    • D37
    • D38
    • D39
    • D40
    • D41
    • D42
    • D43
  • 14.
    • for the Farmers
    • Question:
    • What method can be used to remove
    • pest [Pest Name 1]
    • for rice [Jasmine 105] ?
    Jasmine 105 Rice Name: Pest Name: Pest 1 Search
    • Answer:
    • Method 1
    • Method 2
  • 15. K-service for the Exporter/SME Where does the upland rice grow in? Know Where Southern Region
  • 16. K-service for General People What kind of rice enrich with Vitamin B1 for beriberi disease protecting? Know What 1. Hom Mali (Jasmine rice) 2. Sang Yod ( ข้าวสังข์หยด )
  • 17. K-service for the Farmers What kind of rice that resist Sheath Rot Disease and Ragged Stunt Disease Phatumthanee 60 and Supanburi 90
  • 18. Conclusion: Current and Next Steps:
    • Ontology Workbench Implementation with the experts on rice
    • Do detail in End-users requirement (Q&A)
    • Develop Knowledge Portal about Rice
    • Develop the K-Service (Know-how, Know-why, Know-who, Know-what
    • Evaluate and Test by different end users